CN115524662B - Direction finding time difference joint positioning method, system, electronic equipment and storage medium - Google Patents
Direction finding time difference joint positioning method, system, electronic equipment and storage medium Download PDFInfo
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- CN115524662B CN115524662B CN202211329188.5A CN202211329188A CN115524662B CN 115524662 B CN115524662 B CN 115524662B CN 202211329188 A CN202211329188 A CN 202211329188A CN 115524662 B CN115524662 B CN 115524662B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0257—Hybrid positioning
- G01S5/0263—Hybrid positioning by combining or switching between positions derived from two or more separate positioning systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0273—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves using multipath or indirect path propagation signals in position determination
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/04—Position of source determined by a plurality of spaced direction-finders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/06—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
Abstract
The embodiment of the disclosure provides a direction-finding time difference joint positioning method, a system, electronic equipment and a storage medium, belonging to the field of electronic reconnaissance, wherein the method comprises the following steps: obtaining observation parameter information of a main base station and a secondary base station; and inputting the observation parameter information into a pre-trained target radiation source positioning model to obtain a target radiation source prediction coordinate. The target radiation source positioning model is obtained by training the deep neural network in advance according to training parameter information. Compared with the prior art, the method for obtaining the target radiation source positioning model by training the deep neural network replaces the traditional iterative algorithm, the accuracy and the reliability of the direction-finding time difference combined positioning method are improved, priori information is not relied on, positioning is faster, and the method has very good robustness.
Description
Technical Field
The embodiment of the disclosure belongs to the technical field of electronic reconnaissance, and particularly relates to a direction-finding time difference joint positioning method, a system, electronic equipment and a storage medium.
Background
At least two observation stations are needed for positioning a target based on a direction finding intersection positioning algorithm of an Angle of Arrival (AOA), the time synchronization requirement of a positioning system on the stations is not high, but when the included Angle of two direction finding lines is too small, the two direction finding lines are easily influenced by Angle observation errors, so that a positioning result with lower precision is obtained; whereas the time difference of arrival method based on time difference of arrival (TimeDifference of Arrival, TDOA) requires at least four observation stations for three-dimensional localization of the target, it is demanding for time synchronization between stations. Therefore, the time difference direction finding combined positioning algorithm increases azimuth direction finding on the basis of time difference positioning, and a plurality of receiving stations distributed in different places are used for receiving and processing non-cooperative signals from the same target radiation source, so that the time difference and the angle of the non-cooperative signals of the target radiation source reaching different receiving stations are obtained, a related positioning equation containing the position of the radiation source is established according to the time difference and the angle information, the calculation of the position of the target radiation source is realized, the defects of low-precision positioning result of direction finding positioning, time synchronization severity of time difference positioning and the like are overcome, and a better positioning result is obtained.
For the conventional iterative method, there are mainly local optimization algorithms represented by gradient descent method, newton method, gaussian-newton iterative method, levenberg-Marquardt (Levenberg-Marquardt) method, etc., and global optimization algorithms represented by particle swarm algorithm, genetic evolution algorithm, etc. The method is characterized in that the position of a target radiation source is set as a parameter to be solved, and an optimization algorithm is used for iterating the target function to enable the target function to be lower than a set threshold value or reach the maximum iteration step number to stop iterating, so that the final target radiation source position is obtained. The local optimization algorithm has high convergence speed, is easily influenced by an initial value, and is extremely dependent on prior information; global optimization algorithms, while somewhat reducing the dependence on initial value selection, are still subject to accuracy of a priori information and take longer to iterate.
Therefore, a more accurate, rapid and a priori information free positioning method is needed for the combined direction and time difference positioning system. This is critical to the performance enhancement of distributed passive location electronic scout systems.
Disclosure of Invention
The embodiment of the disclosure aims to at least solve one of the technical problems in the prior art and provides a direction-finding time difference combined positioning method, a system, electronic equipment and a storage medium.
In one aspect, the disclosure provides a direction-finding time difference joint positioning method for implementing target radiation source positioning through a primary base station and a secondary base station, the method includes:
respectively acquiring the observation parameter information of the main base station and the auxiliary base station; the observation parameter information comprises base station actual position information, actual observation angle information and actual base station time difference information;
inputting the observation parameter information of the main base station and the auxiliary base station into a pre-trained target radiation source positioning model respectively to obtain target radiation source prediction coordinates; the target radiation source positioning model is obtained by training the deep neural network in advance according to training parameter information.
Optionally, the target radiation source positioning model is obtained by training the following steps:
generating a sample set of the training parameter information; each training parameter information sample comprises base station position information, observation angle information, base station time difference information and target radiation source coordinates;
and respectively taking the base station position information, the observation angle information and the base station time difference information of the main base station and the auxiliary base station as inputs, and the corresponding target radiation source coordinates as outputs to train the deep neural network so as to obtain the trained target radiation source positioning model.
Optionally, the base station position information includes position coordinates of the primary base station and the secondary base station, and a base station position measurement standard deviation; and/or the number of the groups of groups,
the observation angle information comprises a measured azimuth angle, a measured pitch angle, an azimuth angle measurement standard deviation and a pitch angle measurement standard deviation of the main base station and the auxiliary base station; and/or the number of the groups of groups,
the base station time difference information comprises a measurement time difference and a time difference measurement standard deviation of the secondary base station relative to the main base station.
Optionally, the training the deep neural network with the base station position information, the observation angle information and the base station time difference information as inputs and the target radiation source coordinates as outputs includes:
pre-configuring a loss function, the number of hidden layers and the number of hidden neurons of the deep neural network, the maximum training times, a network training optimizer, a learning rate and a batch size;
taking the base station position information, the observation angle information and the base station time difference information as inputs, taking the target radiation source coordinates as outputs, and training the configured deep neural network;
stopping training when the training of the deep neural network reaches the maximum training times, and taking the network parameter with the minimum loss function in the training as a training result of the deep neural network.
Optionally, the loss function C satisfies the following relation:
wherein n is the total number of training parameter information samples, the sum operation traverses the input x, y (x) of each sample is the output corresponding to x in each sample, a L (x) And outputting a neuron activation value vector for the final layer of the deep neural network.
Optionally, inputting the sample set into a trained target radiation source positioning model, outputting a target radiation source calculation coordinate, and calculating a positioning relative error for evaluating the accuracy of the target radiation source positioning model;
the positioning relative error re satisfies the following relation:
wherein re represents the relative positioning error, (x) cal ,y cal ,z cal ) Calculating coordinates of said target radiation source representing the output, (x) real ,y real ,z real ) Representing the target radiation source coordinates in the sample,representing the coordinates of the master base station, sigma representing the distance between the calculated coordinates of the target radiation source and the coordinates of the target radiation source,/o>Representing the distance between the coordinates of the primary base station and the coordinates of the target radiation source.
Another aspect of the present disclosure provides a direction-finding time difference combined positioning system for realizing positioning of a target radiation source through a primary base station and a secondary base station, wherein the system includes:
the acquisition module is used for acquiring the observation parameter information of the main base station and the auxiliary base station; the observation parameter information comprises base station actual position information, actual observation angle information and actual base station time difference information;
the positioning module is used for inputting the observation parameter information of the main base station and the auxiliary base station into a pre-trained target radiation source positioning model to obtain a target radiation source prediction coordinate; the target radiation source positioning model is obtained by training the deep neural network in advance according to training parameter information.
Optionally, the system further comprises a training module, wherein the training module is used for:
generating a sample set of the training parameter information; each training parameter information sample comprises base station position information, observation angle information, base station time difference information and target radiation source coordinates;
and respectively taking the base station position information, the observation angle information and the base station time difference information of the main base station and the auxiliary base station as inputs, and the corresponding target radiation source coordinates as outputs to train the deep neural network so as to obtain the trained target radiation source positioning model.
Another aspect of the present disclosure provides an electronic device, including:
at least one processor; the method comprises the steps of,
and a memory communicatively coupled to the at least one processor for storing one or more programs that, when executed by the at least one processor, cause the at least one processor to implement the direction-finding time difference joint location method as described above.
A final aspect of the present disclosure provides a computer readable storage medium storing a computer program which when executed by a processor implements a direction finding time difference joint location method as described above.
Compared with the prior art, the direction-finding time difference joint positioning method and system have the advantages that the method for obtaining the target radiation source positioning model by training the deep neural network is adopted to replace a traditional iterative algorithm, the accuracy and reliability of positioning are improved, priori information is not relied on, positioning is faster, and the method and system have very good robustness.
Drawings
FIG. 1 is a flow chart of a combined direction-finding time difference positioning method according to an embodiment of the disclosure;
FIG. 2 is a schematic diagram of a deep neural network according to another embodiment of the present disclosure;
FIG. 3 is a graph showing a loss function versus training time during training of a deep neural network according to another embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a combined positioning system for direction-finding time difference according to another embodiment of the disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to another embodiment of the disclosure.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present disclosure, the present disclosure will be described in further detail with reference to the accompanying drawings and detailed description.
As shown in fig. 1, an embodiment of the present disclosure provides a direction-finding time difference joint positioning method, which includes the following steps:
and S11, training a target radiation source positioning model.
First, a sample set of training parameter information is generated from the reality. Each training parameter information sample comprises base station position information, observation angle information, base station time difference information and target radiation source coordinates.
Specifically, in this step, a sample set including a large number of samples may be generated from an angle of arrival (AOA) model and a time difference of arrival (TDOA) model in a simulation scene, where each sample includes data such as base station position information, observation angle information, base station time difference information, and target radiation source coordinates. The sample sets are randomly disturbed, 4/5 of the data in the sample set form a training set, and 1/5 of the data form a verification set.
The above-mentioned base station position information may include position coordinates of the master base station and each of the slave base stations, and a base station position measurement standard deviation. The observation angle information may include a measured azimuth angle, a measured pitch angle, and azimuth angle and pitch angle measurement standard deviations of the master base station. The base station time difference information may include a measurement time difference and a time difference measurement standard deviation of each secondary base station with respect to the primary base station.
In a specific embodiment, two base stations may be used to locate the target radiation source, and one of the base stations is designated as a main base station and is designated as base station 1; the other base station is designated as a secondary base station and is designated as base station 2. The position coordinates of the two base stations are respectivelyStandard deviation of base station position measurement is sigma s The main base station measures azimuth angle asMeasuring pitch angle θ 1 The azimuth angle of measurement of the secondary base station is +.>Measuring pitch angle θ 2 Standard deviation of azimuth angle measurement isThe pitch angle measurement standard deviation is sigma θ The measurement time difference of the secondary base station relative to the primary base station is deltat 21 Standard deviation of time difference measurement is sigma Δt 。
And then, taking the base station position information, the observation angle information and the base station time difference information as inputs, taking the target radiation source coordinates as outputs, and training a deep neural network (Deep Neural Networks, DNN) to obtain the trained target radiation source positioning model.
Specifically, in this step, the above two base stations will be described as an example, and the base station position coordinates will be describedStandard deviation sigma of base station position measurement s The measured azimuth angle of the main base station is +.>And measuring a pitch angle of θ 1 The azimuth angle of measurement of the secondary base station is +.>And measuring a pitch angle of θ 2 Azimuth measurement standard deviation->Standard deviation sigma of pitch angle measurement θ The measurement time difference of the secondary base station relative to the primary base station is deltat 21 The standard deviation of the time difference measurement is sigma Δt As input to said DNN, i.e. input +.>Taking the target radiation source coordinates as the output of the DNN, i.e. output y= [ x ] t ,y t ,z t ]。
The DNN's loss function, number of hidden layers and number of hidden neurons, maximum number of training, network training optimizer, learning rate, and batch size should also be preconfigured before training the DNN.
Wherein the loss function C satisfies the following relation:
wherein n is the total number of training parameter information samples, the sum operation traverses the input x, y (x) of each sample is the output corresponding to x in each sample, a L (x) And outputting a neuron activation value vector for the DNN final layer.
Training is then started, weighting w for each neuron in DNN using a random gradient descent method l And bias b l (l=2, 3, …, L) learning. First initialize w l And b l (l=2, 3, …, L), the DNN is trained according to the learning rate η, the maximum training number N, and the batch size m set as described above.
And (3) saving DNN parameters with the minimum loss function in the verification set in the training process, and stopping training until the maximum training times are reached.
In some embodiments, after the DNN is trained, the sample set may be input into a trained target radiation source positioning model, the target radiation source calculation coordinates are output, and the positioning relative error is calculated, for evaluating the accuracy of the target radiation source positioning model.
The positioning relative error re satisfies the following relation:
wherein re represents the relative positioning error, (x) cal ,y cal ,z cal ) Calculating coordinates of said target radiation source representing the output, (x) real ,y real ,z real ) Representing the target radiation source coordinates in the sample,representing the coordinates of the primary base station, sigma representing the target radiation source calculated coordinates and the target radiation source coordinatesDistance (L)>Representing the distance between the coordinates of the primary base station and the coordinates of the target radiation source.
And step S12, obtaining the observation parameter information of the main base station and the auxiliary base station. The observation parameter information comprises base station actual position information, actual observation angle information and actual base station time difference information.
Specifically, in this step, the two base stations respectively receive signals from the target radiation source, and form observation parameter information. The observed parameter information comprises base station actual position information, actual observed angle information and actual base station time difference information, wherein the base station actual position information comprises real-time position coordinates of two base stationsStandard deviation sigma 'of base station position measurement' s The method comprises the steps of carrying out a first treatment on the surface of the The actual observation angle information includes the measured azimuth angle +.>Measuring pitch angle θ' 1 Measurement azimuth of secondary base station +>Measuring pitch angle θ' 2 Azimuth measurement standard deviation->Pitch angle measurement standard deviation sigma' θ The method comprises the steps of carrying out a first treatment on the surface of the The actual base station time difference information comprises the measured time difference delta t 'of the secondary base station relative to the main base station' 21 Time difference measurement standard deviation sigma' Δt . And then obtaining the observation parameter information of the two base stations for subsequent processing of data.
And S13, inputting the observation parameter information into a trained target radiation source positioning model to obtain a target radiation source prediction coordinate.
Specifically, the observed parameter information in the step S12 is input into the trained target radiation source positioning model, i.e. the base station is in factInformation of the inter-position, information of the actual observation angle and information of the actual base station time differenceInputting the target radiation source positioning model to obtain an output target radiation source prediction coordinate y= [ x ]' t ,y' t ,z' t ]。
Compared with the prior art, the method for obtaining the target radiation source positioning model by training DNN replaces the traditional iterative algorithm, improves the accuracy and reliability of positioning, does not depend on priori information, is quicker to position, and has very good robustness.
It should be noted that, in the direction-finding time difference combined positioning method of the present embodiment, the step of training the target radiation source positioning model is not necessary, and in a possible implementation manner, the step of training the target radiation source positioning model may be omitted, and a pre-trained target radiation source positioning model may be directly adopted.
The effect of the direction-finding time difference joint positioning method disclosed by the disclosure is further verified and explained through a specific simulation experiment.
Simulation conditions
The simulation conditions are configured by adopting a notebook computer with an Intel (R) Xeon (R) W-10855M CPU@2.80GHz2.81GHz, a memory 64G and a Windows 10 operating system and a NVIDIA Quadro T2000 with Max-Q Design independent display card, and the simulation software is configured by adopting MATLAB (R2021 a) and JetBrainsPyCharm 2018.3.7x64.
(II) simulation content and result analysis
Two base stations are arranged, and the coordinates of the base station 1 are s 1 = (20,3,0) (km), the coordinates of the base station 2 are s 2 = (35,45,0) (km), standard deviation of base station position measurement is σ s ∈[1,10](m). Measuring azimuth of dual stationDouble station measuring pitch angle theta epsilon 1.51,33.23]The standard deviation of azimuth angle measurement is +.>The pitch angle measurement standard deviation is sigma θ ∈[1,5]Standard deviation of time difference measurement of sigma Δt ∈[1,20](ns). Setting the coordinate distribution range of the target radiation source asA total of 388271 samples were generated from the above conditions, taking 2.01 hours. The 388271 samples were considered a sample set. The sample sets are randomly disturbed, 4/5 of the data in the sample set form a training set, and 1/5 of the data form a verification set.
The number of hidden layers is set to be 3, the number of hidden neurons is respectively 150,80 and 60, namely the DNN structure is [15,150,80,60,3], and the structure diagram is shown in figure 2. The maximum training times were set to 1000, the network training optimizer was an SGD optimizer, the learning interest rate was set to 0.0001, and the batch size was set to 40.
And training DNN by using the generated sample set and the set parameters, wherein the final training time is 14.72 hours. The graph of the change of the loss function C with training time is shown in fig. 3.
Respectively inputting the base station position information, the observation angle information and the base station time difference information in the training set and the verification set samples into a target radiation source positioning model to obtain an output target radiation source calculation coordinate (x) cal ,y cal ,z cal ) And then it is matched with the target radiation source coordinates (x real ,y real ,z real ) Base station 1 coordinatesSubstituting the above formula (2) together, calculating the relative positioning error re of each sample, and recording the number of samples in which the relative positioning error re is within 1%, to obtain the percentage of the number of samples in which the relative positioning error is within 1% as shown in table 1.
Table 1:
as can be seen from table 1, in the embodiment of the present disclosure, the positioning accuracy of the target radiation source positioning model trained by using DNN is extremely high, and the method is applicable to a fixed base station and a mobile base station, and has strong robustness and universality.
Another embodiment of the present disclosure provides a direction finding time difference joint positioning system, as shown in fig. 4, comprising:
an acquisition module 401, configured to acquire observation parameter information of a primary base station and a secondary base station; the observation parameter information comprises base station actual position information, actual observation angle information and actual base station time difference information;
the positioning module 402 is configured to input the observation parameter information into a pre-trained target radiation source positioning model, so as to obtain a target radiation source prediction coordinate; the target radiation source positioning model is obtained by training DNN in advance according to training parameter information.
Specifically, the two base stations respectively receive signals from the target radiation source to form observation parameter information. The observed parameter information comprises base station actual position information, actual observed angle information and actual base station time difference information, wherein the base station actual position information comprises real-time position coordinates of two base stationsStandard deviation sigma 'of base station position measurement' s The method comprises the steps of carrying out a first treatment on the surface of the The actual observation angle information includes the actual measured azimuth angle +.>Measuring pitch angle θ' 1 The actual measured azimuth of the secondary base station +.>Measuring pitch angle θ' 2 Azimuth measurement standard deviation->Standard deviation sigma 'of pitch angle measurement' θ The method comprises the steps of carrying out a first treatment on the surface of the The actual base station time difference information comprises the measured time difference delta t 'of the secondary base station relative to the main base station' 21 Time difference measurement standard deviation sigma' Δt . The acquisition module 401 then acquires the observed parameter information of the two base stations, and the positioning module 402 processes the observed parameter information. The positioning module 402 will observe parameter information, i.eInputting the target radiation source positioning model to obtain an output target radiation source prediction coordinate y= [ x ]' t ,y' t ,z' t ]。
According to the direction-finding time difference combined positioning system in the embodiment of the disclosure, the target radiation source is positioned by adopting the method, and an initial value is not required to be selected, so that the direction-finding time difference combined positioning system is more accurate, reliable and rapid compared with a traditional iterative algorithm.
Illustratively, the system further includes a training module 403 for:
generating a sample set of the training parameter information; each training parameter information sample comprises base station position information, observation angle information, base station time difference information and target radiation source coordinates;
and training the DNN by taking the base station position information, the observation angle information and the base station time difference information as inputs and the target radiation source coordinates as outputs to obtain a trained target radiation source positioning model.
Specifically, the training module 403 generates a large number of samples in the simulation scene, randomly scrambles the samples, and trains DNN using the scrambled samples, thereby obtaining the target radiation source positioning model.
According to the embodiment of the disclosure, a large number of samples are generated through the training module, DNN is trained, so that the trained target radiation source positioning model has high precision, and the positioning model can be directly provided for the positioning module for use, so that the positioning system can rapidly and accurately position the target radiation source.
As shown in fig. 5, another embodiment of the present disclosure provides an electronic device, including:
at least one processor 501, and a memory 502 communicatively coupled to the at least one processor 501 for storing one or more programs that, when executed by the at least one processor 501, cause the at least one processor 501 to implement the direction-finding time difference joint location method as described above.
Where the memory and the processor are connected by a bus, the bus may comprise any number of interconnected buses and bridges, the buses connecting the various circuits of the one or more processors and the memory together. The bus may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over the wireless medium via the antenna, which further receives the data and transmits the data to the processor.
The processor is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory may be used to store data used by the processor in performing operations.
According to the electronic equipment, the direction-finding time difference joint positioning method is achieved, and compared with equipment for positioning a target radiation source by using a traditional iterative algorithm, the electronic equipment has better accuracy and reliability and is faster in positioning.
Another embodiment of the present disclosure provides a computer readable storage medium storing a computer program which, when executed by a processor, implements a direction-finding time difference joint positioning method as described above.
The computer readable storage medium may be included in the system and the electronic device of the present disclosure, or may exist alone.
A computer readable storage medium may be any tangible medium that can contain, or store a program that can be electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, apparatus, device, more specific examples including, but not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, an optical fiber, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
The computer readable storage medium may also include a data signal propagated in baseband or as part of a carrier wave, with the computer readable program code embodied therein, specific examples of which include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.
It is to be understood that the above embodiments are merely exemplary embodiments employed to illustrate the principles of the present disclosure, however, the present disclosure is not limited thereto. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the disclosure, and are also considered to be within the scope of the disclosure.
Claims (5)
1. A direction finding time difference joint positioning method, which is used for realizing target radiation source positioning through a main base station and a secondary base station, and comprises the following steps:
respectively acquiring the observation parameter information of the main base station and the auxiliary base station; the observation parameter information comprises base station actual position information, actual observation angle information and actual base station time difference information;
inputting the observation parameter information of the main base station and the auxiliary base station into a pre-trained target radiation source positioning model respectively to obtain target radiation source prediction coordinates; the target radiation source positioning model is obtained by training the following steps:
generating a sample set of training parameter information; each training parameter information sample comprises base station position information, observation angle information, base station time difference information and target radiation source coordinates; the base station position information comprises position coordinates of the main base station and the auxiliary base station and a base station position measurement standard deviation; and/or the observation angle information comprises a measured azimuth angle, a measured pitch angle, an azimuth angle measurement standard deviation and a pitch angle measurement standard deviation of the main base station and the auxiliary base station; and/or, the base station time difference information comprises a measurement time difference and a time difference measurement standard deviation of the secondary base station relative to the main base station;
respectively taking the base station position information, the observation angle information and the base station time difference information of the main base station and the auxiliary base station as inputs, the corresponding target radiation source coordinates as outputs, training a deep neural network, and obtaining a trained target radiation source positioning model, wherein the method comprises the following steps of: pre-configuring a loss function, the number of hidden layers and the number of hidden neurons of the deep neural network, the maximum training times, a network training optimizer, a learning rate and a batch size; taking the base station position information, the observation angle information and the base station time difference information as inputs, taking the target radiation source coordinates as outputs, and training the configured deep neural network; stopping training when the training of the deep neural network reaches the maximum training times, and taking the network parameter with the minimum loss function in the training as a training result of the deep neural network;
inputting the sample set into a trained target radiation source positioning model, outputting a target radiation source calculation coordinate, calculating a positioning relative error, and evaluating the precision of the target radiation source positioning model;
the positioning relative error re satisfies the following relation:
wherein re represents the relative positioning error, (x) cal ,y cal ,z cal ) Calculating coordinates of said target radiation source representing the output, (x) real ,y real ,z real ) Representing the target radiation source coordinates in the sample,representing the coordinates of the master base station, sigma representing the distance between the calculated coordinates of the target radiation source and the coordinates of the target radiation source,/o>Representing the distance between the coordinates of the primary base station and the coordinates of the target radiation source.
2. The method according to claim 1, characterized in that the loss function C satisfies the following relation:
wherein n is the total number of training parameter information samples, the sum operation traverses the input x, y (x) of each sample is the output corresponding to x in each sample, a L (x) And outputting a neuron activation value vector for the final layer of the deep neural network.
3. A direction finding time difference joint location system for achieving target radiation source location through a primary base station and a secondary base station, the system comprising:
the acquisition module is used for acquiring the observation parameter information of the main base station and the auxiliary base station; the observation parameter information comprises base station actual position information, actual observation angle information and actual base station time difference information;
the positioning module is used for inputting the observation parameter information of the main base station and the auxiliary base station into a pre-trained target radiation source positioning model to obtain a target radiation source prediction coordinate; the target radiation source positioning model is obtained by training a deep neural network in advance according to training parameter information
Training module for: generating a sample set of training parameter information; each training parameter information sample comprises base station position information, observation angle information, base station time difference information and target radiation source coordinates; the base station position information comprises position coordinates of the main base station and the auxiliary base station and a base station position measurement standard deviation; and/or the observation angle information comprises a measured azimuth angle, a measured pitch angle, an azimuth angle measurement standard deviation and a pitch angle measurement standard deviation of the main base station and the auxiliary base station; and/or, the base station time difference information comprises a measurement time difference and a time difference measurement standard deviation of the secondary base station relative to the main base station;
respectively taking the base station position information, the observation angle information and the base station time difference information of the main base station and the auxiliary base station as inputs, the corresponding target radiation source coordinates as outputs, training a deep neural network, and obtaining a trained target radiation source positioning model, wherein the method comprises the following steps of: pre-configuring a loss function, the number of hidden layers and the number of hidden neurons of the deep neural network, the maximum training times, a network training optimizer, a learning rate and a batch size; taking the base station position information, the observation angle information and the base station time difference information as inputs, taking the target radiation source coordinates as outputs, and training the configured deep neural network; stopping training when the training of the deep neural network reaches the maximum training times, and taking the network parameter with the minimum loss function in the training as a training result of the deep neural network;
the verification module is used for inputting the sample set into a trained target radiation source positioning model, outputting target radiation source calculation coordinates, calculating positioning relative errors and evaluating the accuracy of the target radiation source positioning model;
the positioning relative error re satisfies the following relation:
wherein re represents the relative positioning error, (x) cal ,y cal ,z cal ) Calculating coordinates of said target radiation source representing the output, (x) real ,y real ,z real ) Representing the target radiation source coordinates in the sample,representing the coordinates of the master base station, sigma representing the distance between the calculated coordinates of the target radiation source and the coordinates of the target radiation source,/o>Representing the distance between the coordinates of the primary base station and the coordinates of the target radiation source.
4. An electronic device, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor for storing one or more programs that, when executed by the at least one processor, cause the at least one processor to implement the joint direction-finding time difference positioning method of any of claims 1 or 2.
5. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the combined direction-finding time difference positioning method according to any one of claims 1 or 2.
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